{"id":3273,"date":"2026-04-13T17:28:28","date_gmt":"2026-04-13T15:28:28","guid":{"rendered":"https:\/\/academics.dii.unipd.it\/phd\/?post_type=tribe_events&#038;p=3273"},"modified":"2026-04-13T17:28:28","modified_gmt":"2026-04-13T15:28:28","slug":"statistics-for-engineers","status":"publish","type":"tribe_events","link":"https:\/\/academics.dii.unipd.it\/phd\/event\/statistics-for-engineers\/","title":{"rendered":"Statistics for Engineers"},"content":{"rendered":"<div class=\"fusion-fullwidth fullwidth-box fusion-builder-row-1 fusion-flex-container nonhundred-percent-fullwidth non-hundred-percent-height-scrolling\" style=\"--awb-border-radius-top-left:0px;--awb-border-radius-top-right:0px;--awb-border-radius-bottom-right:0px;--awb-border-radius-bottom-left:0px;--awb-flex-wrap:wrap;\" ><div class=\"fusion-builder-row fusion-row fusion-flex-align-items-flex-start fusion-flex-content-wrap\" style=\"max-width:calc( 1280px + 0px );margin-left: calc(-0px \/ 2 );margin-right: calc(-0px \/ 2 );\"><div class=\"fusion-layout-column fusion_builder_column fusion-builder-column-0 fusion_builder_column_1_1 1_1 fusion-flex-column\" style=\"--awb-bg-blend:overlay;--awb-bg-size:cover;--awb-width-large:100%;--awb-margin-top-large:0px;--awb-spacing-right-large:0px;--awb-margin-bottom-large:0px;--awb-spacing-left-large:0px;--awb-width-medium:100%;--awb-spacing-right-medium:0px;--awb-spacing-left-medium:0px;--awb-width-small:100%;--awb-spacing-right-small:0px;--awb-spacing-left-small:0px;\"><div class=\"fusion-column-wrapper fusion-flex-justify-content-flex-start fusion-content-layout-column\"><div class=\"fusion-title title fusion-title-1 fusion-sep-none fusion-title-text fusion-title-size-three\"><h3 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;text-transform:capitalize;--fontSize:24;line-height:var(--awb-typography1-line-height);\">Course contents<\/h3><\/div><div class=\"fusion-text fusion-text-1\"><p data-pm-slice=\"1 1 &#091;&#093;\">This course provides a robust foundation in statistical methodologies and data-driven modeling tailored for engineering research. The programme bridges traditional inferential statistics with modern computational intelligence, equipping doctoral candidates with the analytical framework required to extract meaningful insights from complex experimental and industrial datasets.<\/p>\n<p>The curriculum is structured to guide students from foundational descriptive and inferential statistics to more advanced analytical techniques. Initial modules cover linear and non-linear regression models, providing the basis for characterizing system behavior. A significant portion of the course is dedicated to Machine Learning, specifically investigating both supervised and unsupervised algorithms for classification, clustering, and predictive modeling. Additionally, the programme explores the Design of Experiments (DOE), teaching students how to optimize experimental setups and minimize resource expenditure while maximizing data quality. Through hands-on activities with specialized software, students will learn to apply these theoretical concepts to real-world scenarios, culminating in the development of a personal statistical project that integrates the methodologies explored during the lectures with their own doctoral research objectives.<\/p>\n<\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"align-self: center;margin-left: auto;margin-right: auto;margin-top:25px;width:100%;\"><\/div><div class=\"fusion-image-element \" style=\"--awb-caption-title-font-family:var(--h2_typography-font-family);--awb-caption-title-font-weight:var(--h2_typography-font-weight);--awb-caption-title-font-style:var(--h2_typography-font-style);--awb-caption-title-size:var(--h2_typography-font-size);--awb-caption-title-transform:var(--h2_typography-text-transform);--awb-caption-title-line-height:var(--h2_typography-line-height);--awb-caption-title-letter-spacing:var(--h2_typography-letter-spacing);\"><span class=\" fusion-imageframe imageframe-none imageframe-1 hover-type-zoomin\"><a href=\"https:\/\/academics.dii.unipd.it\/phd\/wp-content\/uploads\/sites\/58\/2016\/03\/single_page_course_2.jpg\" class=\"fusion-lightbox\" data-rel=\"iLightbox[de1415e764061b03616]\" data-title=\"Students Studying 3\" title=\"Students Studying 3\"><img decoding=\"async\" width=\"790\" height=\"474\" alt=\"Students Studying 3\" src=\"data:image\/svg+xml,%3Csvg%20xmlns%3D%27http%3A%2F%2Fwww.w3.org%2F2000%2Fsvg%27%20width%3D%27790%27%20height%3D%27474%27%20viewBox%3D%270%200%20790%20474%27%3E%3Crect%20width%3D%27790%27%20height%3D%27474%27%20fill-opacity%3D%220%22%2F%3E%3C%2Fsvg%3E\" data-orig-src=\"https:\/\/academics.dii.unipd.it\/phd\/wp-content\/uploads\/sites\/58\/2016\/03\/single_page_course_2.jpg\" class=\"lazyload img-responsive wp-image-1448\"\/><\/a><\/span><\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"align-self: center;margin-left: auto;margin-right: auto;margin-top:25px;width:100%;\"><\/div><div class=\"fusion-title title fusion-title-2 fusion-sep-none fusion-title-text fusion-title-size-three\"><h3 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;text-transform:capitalize;--fontSize:24;line-height:var(--awb-typography1-line-height);\">Learning goals<\/h3><\/div><div class=\"fusion-text fusion-text-2\"><p>By the conclusion of the course, PhD students will have acquired the fundamental statistical concepts necessary to manage diverse data sources, including those generated within their specific research projects. Participants will develop the proficiency to build and solve physics-based and data-driven systems, mastering both classical inferential methods and advanced machine learning algorithms. Furthermore, the course fosters the ability to use professional, user-friendly statistical software to design, execute, and interpret independent research projects.<\/p>\n<\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"align-self: center;margin-left: auto;margin-right: auto;margin-top:5px;width:100%;\"><\/div><div class=\"fusion-title title fusion-title-3 fusion-sep-none fusion-title-text fusion-title-size-three\"><h3 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;text-transform:capitalize;--fontSize:24;line-height:var(--awb-typography1-line-height);\">Suggested readings<\/h3><\/div><div class=\"fusion-text fusion-text-3\"><p>Materials (slides, datasets, etc.) of the course will be provided by the course leaders.<\/p>\n<\/div><div class=\"fusion-separator fusion-full-width-sep\" style=\"align-self: center;margin-left: auto;margin-right: auto;margin-top:25px;width:100%;\"><\/div><div class=\"fusion-title title fusion-title-4 fusion-sep-none fusion-title-text fusion-title-size-three\"><h3 class=\"fusion-title-heading title-heading-left fusion-responsive-typography-calculated\" style=\"margin:0;text-transform:capitalize;--fontSize:24;line-height:var(--awb-typography1-line-height);\">Notes<\/h3><\/div><div class=\"fusion-text fusion-text-4\"><p>The course is structured into 2 online (February) and a Summer School of 4 days (June). The Summer School will take place in Villa San Giuseppe, Monguelfo, Bolzano province. During the course an introduction to the use of the following statistical software will be presented:<\/p>\n<ul>\n<li>R and BlueSky, both open-source software.<\/li>\n<li>MINITAB, licensed to University of Padova.<\/li>\n<\/ul>\n<\/div><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":44,"featured_media":3274,"template":"","meta":{"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[34],"class_list":["post-3273","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-event","cat_event"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Statistics for Engineers - PHD in Industrial Engineering<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/academics.dii.unipd.it\/phd\/event\/statistics-for-engineers\/\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Statistics for Engineers - 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